Structural Features Of Proteins Research Articles

The underlying chemistry of electronegative LDL's atherogenicity.

Electronegative low-density lipoprotein (LDL) found in human plasma is highly atherogenic, and its level is elevated in individuals with increased cardiovascular risk. In this review, we summarize the available data regarding the elevation of the levels of electronegative LDL in the plasma of patients with various diseases. In addition, we discuss the harmful effects and underlying mechanisms of electronegative LDL in various cell types. We also highlight the known biochemical properties of electronegative LDL that may contribute to its atherogenic functions, including its lipid and protein composition, enzymatic activities, and structural features. Given the increasing recognition of electronegative LDL as a potential biomarker and therapeutic target for the prevention of cardiovascular disease, key future goals include the development of a standard method for the detection of electronegative LDL that can be used in a large-scale population survey and the identification and testing of strategies for eliminating electronegative LDL from the blood.

Molecular properties and medical applications of peptide nucleic acids.

Peptide Nucleic Acids (PNAs) are molecules combining structural features of proteins and nucleic acid. They resemble DNA or RNA by forming helical polyamides containing nitrogen bases attached to the backbone consisting of N-(2-aminoethyl)-glycine monomers, which mimics the alternating ribose-phosphodiester-backbone of a nucleic acid. Because PNAs bind exceptionally strong to complementary DNA or RNA sequences obeying Watson-Crick base paring, they became attractive candidates for antisense and antigen therapies. PNAs are also being tested as novel antibiotics, gene-activating agents, and as molecular probes for FISH and imaging or biosensors used in diagnostics. Although PNAs offer many exiting medical applications, improving their cellular uptake and developing specific delivery strategies is crucial for a successful entry in the clinic in the near future.

From sequence to enzyme mechanism using multi-label machine learning.

BackgroundIn this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling.ResultsIn this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm.ConclusionWe find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.

Specificities of human CD4+ T cell responses to an inactivated flavivirus vaccine and infection: correlation with structure and epitope prediction.

Tick-borne encephalitis (TBE) virus is endemic in large parts of Europe and Central and Eastern Asia and causes more than 10,000 annual cases of neurological disease in humans. It is closely related to the mosquito-borne yellow fever, dengue, Japanese encephalitis, and West Nile viruses, and vaccination with an inactivated whole-virus vaccine can effectively prevent clinical disease. Neutralizing antibodies are directed to the viral envelope protein (E) and an accepted correlate of immunity. However, data on the specificities of CD4(+) T cells that recognize epitopes in the viral structural proteins and thus can provide direct help to the B cells producing E-specific antibodies are lacking. We therefore conducted a study on the CD4(+) T cell response against the virion proteins in vaccinated people in comparison to TBE patients. The data obtained with overlapping peptides in interleukin-2 (IL-2) enzyme-linked immunosorbent spot (ELISpot) assays were analyzed in relation to the three-dimensional structures of the capsid (C) and E proteins as well as to epitope predictions based on major histocompatibility complex (MHC) class II peptide affinities. In the C protein, peptides corresponding to two out of four alpha helices dominated the response in both vaccinees and patients, whereas in the E protein concordance of immunodominance was restricted to peptides of a single domain (domain III). Epitope predictions were much better for C than for E and were especially erroneous for the transmembrane regions. Our data provide evidence for a strong impact of protein structural features that influence peptide processing, contributing to the discrepancies observed between experimentally determined and computer-predicted CD4(+) T cell epitopes. Importance: Tick-borne encephalitis virus is endemic in large parts of Europe and Asia and causes more than 10,000 annual cases of neurological disease in humans. It is closely related to yellow fever, dengue, Japanese encephalitis, and West Nile viruses, and vaccination with an inactivated vaccine can effectively prevent disease. Both vaccination and natural infection induce the formation of antibodies to a viral surface protein that neutralize the infectivity of the virus and mediate protection. B lymphocytes synthesizing these antibodies require help from other lymphocytes (helper T cells) which recognize small peptides derived from proteins contained in the viral particle. Which of these peptides dominate immune responses to vaccination and infection, however, was unknown. In our study we demonstrate which parts of the proteins contribute most strongly to the helper T cell response, highlight specific weaknesses of currently available approaches for their prediction, and demonstrate similarities and differences between vaccination and infection.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

BackgroundIt is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models.ResultsWe developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark.ConclusionSMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Redundancy-weighting for better inference of protein structural features.

Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of the PDB. While alleviating biases, this solution hides the many-to-many relations between sequences and structures. That is, non-redundant datasets conceal the diversity of sequences that share the same fold and the existence of multiple conformations for the same protein. A particularly disturbing aspect of non-redundant subsets is that they hardly benefit from the rapid pace of protein structure determination, as most newly solved structures fall within existing families. In this study we explore the concept of redundancy-weighted datasets, originally suggested by Miyazawa and Jernigan. Redundancy-weighted datasets include all available structures and associate them (or features thereof) with weights that are inversely proportional to the number of their homologs. Here, we provide the first systematic comparison of redundancy-weighted datasets with non-redundant ones. We test three weighting schemes and show that the distributions of structural features that they produce are smoother (having higher entropy) compared with the distributions inferred from non-redundant datasets. We further show that these smoothed distributions are both more robust and more correct than their non-redundant counterparts. We suggest that the better distributions, inferred using redundancy-weighting, may improve the accuracy of knowledge-based potentials and increase the power of protein structure prediction methods. Consequently, they may enhance model-driven molecular biology.

Novel methods based on 13C detection to study intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by highly flexible solvent exposed backbones and can sample many different conformations. These properties confer them functional advantages, complementary to those of folded proteins, which need to be characterized to expand our view of how protein structural and dynamic features affect function beyond the static picture of a single well defined 3D structure that has influenced so much our way of thinking.NMR spectroscopy provides a unique tool for the atomic resolution characterization of highly flexible macromolecules in general and of IDPs in particular. The peculiar properties of IDPs however have profound effects on spectroscopic parameters. It is thus worth thinking about these aspects to make the best use of the great potential of NMR spectroscopy to contribute to this fascinating field of research. In particular, after many years of dealing with exclusively heteronuclear NMR experiments based on 13C direct detection, we would like here to address their relevance when studying IDPs.

Differential role of molten globule and protein folding in distinguishing unique features of botulinum neurotoxin

Botulinum neurotoxins (BoNTs) are proteins of great interest not only because of their extreme toxicity but also paradoxically for their therapeutic applications. All the known serotypes (A-G) have varying degrees of longevity and potency inside the neuronal cell. Differential chemical modifications such as phosphorylation and ubiquitination have been suggested as possible mechanisms for their longevity, but the molecular basis of the longevity remains unclear. Since the endopeptidase domain (light chain; LC) of toxin apparently survives inside the neuronal cells for months, it is important to examine the structural features of this domain to understand its resistance to intracellular degradation. Published crystal structures (both botulinum neurotoxins and endopeptidase domain) have not provided adequate explanation for the intracellular longevity of the domain. Structural features obtained from spectroscopic analysis of LCA and LCB were similar, and a PRIME (PReImminent Molten Globule Enzyme) conformation appears to be responsible for their optimal enzymatic activity at 37°C. LCE, on the other hand, was although optimally active at 37°C, but its active conformation differed from the PRIME conformation of LCA and LCB. This study establishes and confirms our earlier finding that an optimally active conformation of these proteins in the form of PRIME exists for the most poisonous poison, botulinum neurotoxin. There are substantial variations in the structural and functional characteristics of these active molten globule related structures among the three BoNT endopeptidases examined. These differential conformations of LCs are important in understanding the fundamental structural features of proteins, and their possible connection to intracellular longevity could provide significant clues for devising new countermeasures and effective therapeutics.

The impact of structural integrity and route of administration on the antibody specificity against three cow's milk allergens - a study in Brown Norway rats.

BackgroundCharacterisation of the specific antibody response, including the epitope binding pattern, is an essential task for understanding the molecular mechanisms of food allergy. Examination of antibody formation in a controlled environment requires animal models. The purpose of this study was to examine the amount and types of antibodies raised against three cow’s milk allergens; β-lactoglobulin (BLG), α-lactalbumin (ALA) and β-casein upon oral or intraperitoneal (i.p.) administration. A special focus was given to the relative amount of antibodies raised against linear versus conformational epitopes.MethodsSpecific antibodies were raised in Brown Norway (BN) rats. BN rats were dosed either (1) i.p. with the purified native cow’s milk allergens or (2) orally with skimmed milk powder (SMP) alone or together with gluten, without the use of adjuvants. The allergens were denatured by reduction and alkylation, resulting in unfolding of the primary structure and a consequential loss of conformational epitopes. The specific IgG1 and IgE responses were analysed against both the native and denatured form of the three cow’s milk allergens, thus allowing examination of the relative amount of linear versus conformational epitopes.ResultsThe inherent capacity to induce specific IgG1 and IgE antibodies were rather similar upon i.p. administration for the three cow’s milk allergens, with BLG = ALA > β-casein. Larger differences were found between the allergens upon oral administration, with BLG > ALA > β-casein. Co-administration of SMP and gluten had a great impact on the specific antibody response, resulting in a significant reduced amount of antibodies. Together results indicated that most antibodies were raised against conformational epitopes irrespectively of the administration route, though the relative proportions between linear and conformational epitopes differed remarkably between the allergens.ConclusionsThis study showed that the three-dimensional (3D) structure has a significant impact on the antibodies raised for both systemic and orally administered allergens. A remarkable difference in the antibody binding patterns against linear and conformational epitope was seen between the allergens, indicating that the structural characteristics of proteins may heavily affect the induced antibody response.

Simple Rules Imposed on a Primitive Cubic Lattice Robustly Generate Structures that Mimic Features of Real Proteins

We introduce a set of simple and well-defined rules, which produce protein-like networks when they are imposed on a primitive cubic lattice. The resulting artificial structures successfully mimic the geometric and topological features of real proteins, and therefore, provide the opportunity to understand characteristics of protein structures and lead to the creation of synthetic proteins. The proposed method does not involve a chain-fitting step and does not require individual set of reference structures. We start with a cubic lattice, whose lattice sites contain beads representing protein residues. Many cubic lattices are emptied up to 60% vacancy concentration by randomly removing beads while maintaining a connected network of occupied sites. The maximum vacancy concentration of 60% was obtained from first and second nearest neighbor occupancies of real protein residues. A Reverse Monte-Carlo/Simulated Annealing (RMC/SA) simulation that is constrained to fit the average radial distribution function of residues of 278 proteins is then performed. Results indicate that the RMC/SA procedure recovers the average radial distribution function without disturbing other structural properties such as bond orientational order parameters and network topology. Based on various structural properties, our results indicate that these artificially created structures closely resemble real residue networks.

Using hidden Markov models to predict DNA-binding proteins with sequence and structure information

In the post-genome period, the protein domain structures are published rapidly, but they have not been studied comprehensively. To figure out the cell function, the protein---DNA interactions decrypt the protein domain structures in recent research. Several machine-learning based methods are applied to the issue; however, they are not efficient to translate the tertiary structure characteristics of proteins into appropriate features for predicting the DNA-binding proteins. In this work, a novel machine-learning approach based on hidden Markov models identifies the characteristics of DNA-binding proteins with their amino acid sequences and tertiary structures. After we distill the features from DNA-binding proteins, a support vector machine based classifier predicts general DNA-binding proteins with the accuracy of 88.45 % through fivefolds cross-validation. Furthermore, we construct a response element specific classifier for predicting response element specific DNA-binding proteins, and the performance achieves the precision of 96.57 % with recall rate as 88.83 % in average. To verify the prediction of DNA-binding proteins, we used the DNA-binding proteins from MCF-7 that are likely to bind with estrogen response elements (ERE), and the results show that our methods can apply to practice.

Modules Identification in Protein Structures: The Topological and Geometrical Solutions

The identification of modules in protein structures has major relevance in structural biology, with consequences in protein stability and functional classification, adding new perspectives in drug design. In this work, we present the comparison between a topological (spectral clustering) and a geometrical (k-means) approach to module identification, in the frame of a multiscale analysis of the protein architecture principles. The global consistency of an adjacency matrix based technique (spectral clustering) and a method based on full rank geometrical information (k-means) give a proof-of-concept of the relevance of protein contact networks in structure determination. The peculiar "small-world" character of protein contact graphs is established as well, pointing to average shortest path as a mesoscopic crucial variable to maximize the efficiency of within-molecule signal transmission. The specific nature of protein architecture indicates topological approach as the most proper one to highlight protein functional domains, and two new representations linking sequence and topological role of aminoacids are demonstrated to be of use for protein structural analysis. Here we present a case study regarding azurin, a small copper protein implied in the Pseudomonas aeruginosa respiratory chain. Its pocket molecular shape and its electron transfer function have challenged the method, highlighting its potentiality to catch jointly the structure and function features of protein structures through their decomposition into modules.

English

The current reach of genomics extends facilitated identification of microbial virulence factors, a primary objective for antimicrobial drug and vaccine design. Many putative proteins are yet to be identified which can act as potent drug targets. There is lack and limitation of methods which appropriately combine several omics ways for putative and new drug target identification. The study emphasizes a combined bioinformatic and theoretical method of screening unique and putative drug targets, lacking similarity with experimentally reported essential genes and drug targets. Synteny based comparison was carried out with 11 streptococci considering S. gordonii as reference genome. It revealed 534 non-homologous genes of which 334 were putative. Similarity search against host proteome, metabolic pathway annotation and subcellular localization predication identified 16 potent drug targets. This is a first attempt of several combinational approaches of similarity search with target protein structural features for screening drug targets, yielding a pipeline which can be substantiated to other human pathogens.

Effect of pH and Pepsin Limited Hydrolysis on the Structure and Functional Properties of Soybean Protein Hydrolysates

Effects of limited enzymatic hydrolysis with pepsin on the functional properties and structure characteristics of soybean proteins were investigated. Hydrolysates with different incubation time (10 to 900 min) were prepared. Results showed that SPI hydrolyzed for 60 min exhibited the best emulsibility and the ability of resisting freezing/thawing. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis proved that pepsin can degrade glycinin but had little effect on the α' subunit of β-conglycinin. The structure unfolding reached the largest extent after incubation for 60 min and the soluble and flexible aggregates were formed. After 120 min, glycinin was degraded totally and β-conglycinin formed insoluble aggregates. Moreover, 2 methods were applied for the deactivation of pepsin to obtain final hydrolysates at pH 2.0 and 7.0, respectively. The structure analysis revealed that the unfolding extent and structure characteristic were different in these 2 conditions. When adjusting the pH value from 2.0 to 7.0, the unfolding protein molecular would reaggregate again at pH 7.0 due to the charge neutralization, and the hydrodynamic diameter and λmax absorbance decreased compared to pH 2.0. Moreover, some of the insoluble aggregates formed at pH 2.0 became soluble at pH 7.0, because of the salt-in phenomenon.

Studies on Brassica carinata Seed. 1. Protein Molecular Structure in Relation to Protein Nutritive Values and Metabolic Characteristics

The objectives of this study were to investigate (1) the protein chemical profile, (2) the protein subfractions partitioned by the Cornell Net Carbohydrate and Protein System (CNCPS), (3) the rumen crude protein (CP) degradation kinetics, (4) the protein supply predicted by the DVE/OEB system, (5) the protein structural features using a Fourier transform infrared (FTIR) spectroscopic technique with attenuated total reflectance (ATR), and (6) the correlations between protein intrinsic structural features and nutritional profiles in three strains of Brassica carinata in yellow and brown seed coats, with comparison to canola seed as a reference. The results showed that carinata seed strains were different in both nutritional values and IR absorbance within the protein spectral region (ca. 1720-1482 cm(-1)). The comparison between yellow and brown B. carinata seeds indicated that the former was lower in acid detergent insoluble crude protein (ADICP; P = 0.002) and undegradable protein fraction (PC; P = 0.002) and greater in the degradable (D) fraction (P = 0.004) and true absorbed protein in the small intestine (DVE; P = 0.02) as well as feed milk value (FMV; P = 0.02) than the latter. The brown canola seed (Brassica napus L.) was also not in full accordance with B. carinata seed on these parameters. The FTIR studies showed significant differences in protein amide II peak height, amide I peak area, and β-sheet height among different B. carinata strains. However, multivariate spectral analyses indicated a similarity in protein structural makeup in these four kinds of oilseed. The not very strong correlations shown in this study implied that the limited sample size and narrow range in biological and spectral variation might be responses for the weak relationships between chemical profile and mid-IR spectral data. Further studies using sufficient samples with wide and diverse range in nutritional properties are needed to illustrate the actual relationship between spectroscopic data and nutritional profiles in oilseeds.

In-Depth Glycoproteomic Characterization of γ-Conglutin by High-Resolution Accurate Mass Spectrometry

The molecular characterization of bioactive food components is necessary for understanding the mechanisms of their beneficial or detrimental effects on human health. This study focused on γ-conglutin, a well-known lupin seed N-glycoprotein with health-promoting properties and controversial allergenic potential. Given the importance of N-glycosylation for the functional and structural characteristics of proteins, we studied the purified protein by a mass spectrometry-based glycoproteomic approach able to identify the structure, micro-heterogeneity and attachment site of the bound N-glycan(s), and to provide extensive coverage of the protein sequence. The peptide/N-glycopeptide mixtures generated by enzymatic digestion (with or without N-deglycosylation) were analyzed by high-resolution accurate mass liquid chromatography–multi-stage mass spectrometry. The four main micro-heterogeneous variants of the single N-glycan bound to γ-conglutin were identified as Man2(Xyl) (Fuc) GlcNAc2, Man3(Xyl) (Fuc) GlcNAc2, GlcNAcMan3(Xyl) (Fuc) GlcNAc2 and GlcNAc 2Man3(Xyl) (Fuc) GlcNAc2. These carry both core β1,2-xylose and core α1-3-fucose (well known Cross-Reactive Carbohydrate Determinants), but corresponding fucose-free variants were also identified as minor components. The N-glycan was proven to reside on Asn131, one of the two potential N-glycosylation sites. The extensive coverage of the γ-conglutin amino acid sequence suggested three alternative N-termini of the small subunit, that were later confirmed by direct-infusion Orbitrap mass spectrometry analysis of the intact subunit.

Tandem-repeat proteins: regularity plus modularity equals design-ability

Researchers in the field of rational protein design face a significant challenge, which arises from the two defining and inter-related features of typical globular protein structures, namely topological complexity and cooperativity. In striking contrast to globular proteins, tandem repeat proteins, such as ankyrin, tetratricopeptide and leucine-rich repeats, have regular, modular, linearly arrayed structures which makes it especially straightforward to dissect and redesign their properties. Here we review what we have learnt about the biophysics of natural repeat proteins and recent progress in applying that knowledge to engineer the thermodynamics, folding pathways and molecular recognition properties of tandem repeat proteins, and we discuss the wealth of possibilities presented for the extension of this modular construction process to build new molecules for use in medicine and biotechnology.

Hofmeister Salts Recover a Misfolded Multiprotein Complex for Subsequent Structural Measurements in the Gas Phase

Proteins are the workhorses of the cell, the functions of which are largely predicated on their structures and dynamics. Detailed knowledge of these attributes has enabled countless breakthroughs in human health and disease.[1] Many aspects, however, of protein structure remain poorly understood, including their high-order interactions.[2] This knowledge gap drives the development of new tools capable of protein structure characterization, some of which operate by measuring desolvated protein structure.[3] While desolvation enables the application of powerful analytical techniques that cannot be used in a solvated environment, and recent results indicate that many features of native protein structure survive in the gas-phase,[4] desolvation may also act to obfuscate critical details of protein conformation.8 Recently, multiple strategies have emerged for observing labile protein structures in the absence of bulk water and have proven useful in stabilizing protein-small molecule interactions, globular proteins, and their complexes.[5] Here, we report the first evidence that a misfolded protein complex, which exists both in solution and in the gas-phase, can be recovered back to a ‘native-like’ structure through the addition of salts prior to desorption/ionization. These data represent the first time that such a solution-phase multiprotein folding equilibrium is captured by gas-phase measurements.

Correlation Spectroscopy and Molecular Dynamics Simulations to Study the Structural Features of Proteins

In this work, we used a combination of fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulation methodologies to acquire structural information on pH-induced unfolding of the maltotriose-binding protein from Thermus thermophilus (MalE2). FCS has emerged as a powerful technique for characterizing the dynamics of molecules and it is, in fact, used to study molecular diffusion on timescale of microsecond and longer. Our results showed that keeping temperature constant, the protein diffusion coefficient decreased from 84±4 µm2/s to 44±3 µm2/s when pH was changed from 7.0 to 4.0. An even more marked decrease of the MalE2 diffusion coefficient (31±3 µm2/s) was registered when pH was raised from 7.0 to 10.0. According to the size of MalE2 (a monomeric protein with a molecular weight of 43 kDa) as well as of its globular native shape, the values of 44 µm2/s and 31 µm2/s could be ascribed to deformations of the protein structure, which enhances its propensity to form aggregates at extreme pH values. The obtained fluorescence correlation data, corroborated by circular dichroism, fluorescence emission and light-scattering experiments, are discussed together with the MD simulations results.

Analysis of Conformational Motions and Residue Fluctuations for Escherichia coli Ribose-Binding Protein Revealed with Elastic Network Models

The ribose-binding protein (RBP) is a sugar-binding bacterial periplasmic protein whose function is associated with a large allosteric conformational change from an open to a closed conformation upon binding to ribose. The open (ligand-free) and closed (ligand-bound) forms of RBP have been found. Here we investigate the conformational motions and residue fluctuations of the RBP by analyzing the modes of motion with two coarse-grained elastic network models, the Gaussian Network Model (GNM) and Anisotropic Network Model (ANM). The calculated B-factors in both the calculated models are in good agreement with the experimentally determined B-factors in X-ray crystal structures. The slowest mode analysis by GNM shows that both forms have the same motion hinge axes around residues Ser103, Gln235, Asp264 and the two domains of both structures have similar fluctuation range. The superposition of the first three dominant modes of ANM, consisting of the rotating, bending and twisting motions of the two forms, accounts for large rearrangement of domains from the ligand-free (open) to ligand-bound (closed) conformation and thus constitutes a critical component of the RBP’s functions. By analyzing cross-correlations between residue fluctuation and the difference-distance plot, it is revealed that the conformational change can be described as a rigid rotation of the two domains with respect to each other, whereas the internal structure of the two domains remains largely intact. The results directly indicate that the dominant dynamic characteristics of protein structures can be captured from their static native state using coarse-grained models.